Motion Estimation Using Stereovision and Extended Kalman Filter

To address the issue of collision avoidance in intelligent cars, in this paper, we present a motion estimation method that uses binocular stereovision and extended Kalman filtering (EKF). In the detection stage, the region of interest is identified by segmenting obstacles based on their position by the three-dimensional reconstruction capability of stereovision. In the tracking stage, the optical flow is used to track the edge points within the target area. EKF is used to make the prediction and enable the measurement models to fuse the vehicle motion, optical flow, and disparity, and thereby obtain the optimized target position and velocity. We established a relative motion model with respect to the self-driving vehicle and objects. This method employs edge-point constraints and the random sample consensus algorithm to eliminate unreliable tracking points. We tested and verified the performance of the proposed method using traffic scenarios provided by the KITTI public data set.